# Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks

arXiv: https://arxiv.org/abs/2403.17534 · HTML: https://mlsanity.com/paper/2403.17534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis](https://mlsanity.com/paper/2512.00214/) (2025)
- [Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions](https://mlsanity.com/paper/2604.23924/) (2026)

## Abstract (arXiv)

Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks, in order to create an easy-to-understand corpus-based grammar. More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules. For that, we use a linear classifier to extract the most salient features that predict the linguistic phenomena under study. We associate statistical information to each rule, and we compare the ranking of the model's results to those of other quantitative and statistical measures. Our method captures both well-known and less well-known significant grammar rules in Spanish, French, and Wolof.